{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- This should be added to the overrides/main.html and improved-->\n",
    "<div class=\"grid cards\" markdown>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
    "<a href=\"https://colab.research.google.com/github/AmbiqAI/heartkit/blob/main/docs/guides/train-arrhythmia-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    View in Colab\n",
    "</a>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 496 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3 .3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5 .3-6.2 2.3zm44.2-1.7c-2.9 .7-4.9 2.6-4.6 4.9 .3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3 .7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3 .3 2.9 2.3 3.9 1.6 1 3.6 .7 4.3-.7 .7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3 .7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3 .7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>\n",
    "<a href=\"https://github.com/AmbiqAI/heartkit/blob/main/docs/guides/train-arrhythmia-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    GitHub source\n",
    "</a>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train ECG Arrhythmia Classifier\n",
    "\n",
    "__Date created:__ 2024/07/17 \n",
    "\n",
    "__Last Modified:__ 2024/07/17 \n",
    "\n",
    "__Description:__ Train, evaluate, and export 4-stage ECG arrhythmia classifier\n",
    "\n",
    "## Overview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this guide, we will train an ECG-based arrhythmia classifier that uses an EfficientNetV2 inspired model. The classifier is trained on raw ECG data and is able to discern normal sinus rhythm (NSR), sinus bradycardia (SBRAD), atrial fibrillation (AFIB), and general supraventricular tachycardia (GSVT).\n",
    "\n",
    "__Input__\n",
    "\n",
    "- **Sensor**: ECG\n",
    "- **Location**: Wrist\n",
    "- **Sampling Rate**: 100 Hz\n",
    "- **Frame Size**: 5 seconds\n",
    "\n",
    "__Class Mapping__\n",
    "\n",
    "Identify rhythm into one of four categories: SR, SBRAD, AFIB, GSVT.\n",
    "\n",
    "| Base Class     | Target Class | Label                     |\n",
    "| -------------- | ------------ | ------------------------- |\n",
    "| 0-SR           | 0            | Sinus Rhythm (SR)         |\n",
    "| 1-SBRAD        | 1            | Sinus Bradycardia (SBRAD) |\n",
    "| 7-AFIB, 8-AFL  | 2            | AFIB/AFL (AFIB) |\n",
    "| 2-STACH, 5-SVT | 3            | General supraventricular tachycardia (GSVT) |\n",
    "\n",
    "__Datasets__\n",
    "\n",
    "- **[LSAD](https://ambiqai.github.io/heartkit/datasets/lsad/)**: The Large Scale Rhythm Database (LSAD) is a large publicly available electrocardiography dataset. It contains 10 second, 12-lead ECGs of 45,152 patients with a 500 Hz sampling rate. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "import IPython\n",
    "import contextlib\n",
    "from pathlib import Path\n",
    "import tempfile\n",
    "import keras\n",
    "import heartkit as hk\n",
    "import neuralspot_edge as nse\n",
    "import plotly.io as pio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be sure to set the dataset path to the correct location\n",
    "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n",
    "\n",
    "plot_theme = hk.utils.dark_theme\n",
    "nse.utils.silence_tensorflow()\n",
    "hk.utils.setup_plotting(plot_theme)\n",
    "logger = nse.utils.setup_logger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure datasets\n",
    "\n",
    "We are going to train our model using the [Large scale Arrhythmia dataset](https://ambiqai.github.io/heartkit/datasets/lsad/). This dataset uses the slug __lsad__ within HeartKit. We will download the dataset if it is not already available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [hk.NamedParams(\n",
    "    name=\"lsad\",\n",
    "    params=dict(\n",
    "        path=datasets_dir / \"lsad\",\n",
    "    )\n",
    ")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target classes\n",
    "\n",
    "For this task, we are going to classify ECG signals into one of four classes: \n",
    "\n",
    "* Sinus Rhytm: Normal ECG signal (SR) \n",
    "* Sinus Bradycardia: Slow heart rate (SB) \n",
    "* Atrial Flutter/Fibrillation: Irregular heart rate (AFIB) \n",
    "* General Supra-Ventricular Tachycardia: Fast heart rate (GSVT)\n",
    "\n",
    "HeartKit already provides a number of heart rhythms. We will provide a class mapping for the four classes we are interested in. We will also provide class names for display purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_map = {\n",
    "    hk.tasks.HKRhythm.sr: 0,\n",
    "    hk.tasks.HKRhythm.sbrad: 1,\n",
    "    hk.tasks.HKRhythm.afib: 2,\n",
    "    hk.tasks.HKRhythm.aflut: 2,\n",
    "    hk.tasks.HKRhythm.stach: 3,\n",
    "    hk.tasks.HKRhythm.svt: 3\n",
    "}\n",
    "\n",
    "class_names=[\n",
    "    \"SR\",\n",
    "    \"SB\",\n",
    "    \"AFIB\",\n",
    "    \"GSVT\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess pipeline\n",
    "\n",
    "We will preprocess the ECG signals by applying the following steps:\n",
    "* Apply Z-score normalization w/ epsilon to avoid division by zero\n",
    "\n",
    "The task accepts a list of preprocessing functions that will be applied to the input data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocesses = [\n",
    "    hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define EfficientNetV2 model architecture\n",
    "\n",
    "For this task, we are going to leverage a customized __EfficientNetV2__ model architecture that is smaller and can handle 1D signals. The model consists of 6 MBConv style blocks with a depth of 2. Each block leverages squeeze-and-excitation mechanism w/ ratio of 4 to improve the model's performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "architecture = hk.NamedParams(\n",
    "    name=\"efficientnetv2\",\n",
    "    params=dict(\n",
    "        input_filters=24,\n",
    "        input_kernel_size=(1, 9),\n",
    "        input_strides=(1, 2),\n",
    "        blocks=[\n",
    "            dict(filters=24, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
    "            dict(filters=32, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
    "            dict(filters=48, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
    "            dict(filters=64, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
    "            dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n",
    "            dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4)\n",
    "        ],\n",
    "        output_filters=0,\n",
    "        include_top=True,\n",
    "        use_logits=True\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task configuration\n",
    "\n",
    "Here we provide the complete configuration for the task. This includes the dataset configuration, preprocessing pipeline, model architecture, and training parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = hk.HKTaskParams(\n",
    "    # Common arguments\n",
    "    name=\"hk-4-stage-rhythm\",\n",
    "    job_dir=Path(tempfile.gettempdir()) / \"hk-4-stage-rhythm\",\n",
    "    # Dataset arguments\n",
    "    datasets=datasets,\n",
    "    # Signal arguments\n",
    "    sampling_rate=100,\n",
    "    frame_size=800,\n",
    "    # Dataloader arguments\n",
    "    samples_per_patient=5,\n",
    "    val_samples_per_patient=5,\n",
    "    test_samples_per_patient=5,\n",
    "    # Preprocessing/Augmentation arguments\n",
    "    preprocesses=preprocesses,\n",
    "    # Class arguments\n",
    "    num_classes=len(class_names),\n",
    "    class_map=class_map,\n",
    "    class_names=class_names,\n",
    "    # Split arguments\n",
    "    val_patients=0.2,\n",
    "    val_size=20000,\n",
    "    test_size=20000,\n",
    "    val_file=\"val.tfds\",\n",
    "    test_file=\"val.tfds\",\n",
    "    # Model arguments\n",
    "    model_file=\"model.keras\",\n",
    "    architecture=architecture,\n",
    "    # Training parameters\n",
    "    lr_rate=1e-3,\n",
    "    lr_cycles=1,\n",
    "    batch_size=256,\n",
    "    buffer_size=25000,\n",
    "    epochs=100,\n",
    "    steps_per_epoch=50,\n",
    "    val_metric=\"loss\",\n",
    "    class_weights=\"balanced\",\n",
    "    # Evaluation arguments\n",
    "    threshold=0.5,\n",
    "    test_metric_threshold=0.98,\n",
    "    # Export parameters\n",
    "    tflm_var_name=\"ecg_rhythm_flatbuffer\",\n",
    "    tflm_file=\"ecg_rhythm_flatbuffer.h\",\n",
    "    # Demo params\n",
    "    backend=\"pc\",\n",
    "    demo_size=800,\n",
    "    display_report=False,\n",
    "    # Extra arguments\n",
    "    verbose=1,\n",
    "    seed=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load rhythm task \n",
    "\n",
    "HeartKit provides a __TaskFactory__ that includes a number ready-to-use tasks. Each task provides methods for training, evaluating, exporting, and demoing. We will grab the __rhythm__ task and configure it for our use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = hk.TaskFactory.get('rhythm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download datasets\n",
    "\n",
    "We will download the datasets needed for the task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "task.download(params=params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the model\n",
    "\n",
    "Lets quickly instantiate and visualize the first few layers of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723841711.256117  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.276561  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.276664  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.278058  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.278130  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.278176  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.327697  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.327778  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723841711.327833  789182 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"EfficientNetV2\"</span>\n",
       "</pre>\n"
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       "\u001b[1mModel: \"EfficientNetV2\"\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inputs (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">800</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">800</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ inputs[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.conv (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">216</span> │ reshape[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.bn             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ stem.conv[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.act            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ stem.bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">216</span> │ stem.act[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DepthwiseConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp.… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │         <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span> │ stage1.mbconv1.d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp.… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ stage1.mbconv1.d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ stage1.mbconv1.d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.se.… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.se.… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span> │ stage1.mbconv1.s… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ inputs (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m800\u001b[0m, \u001b[38;5;34m1\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m800\u001b[0m, \u001b[38;5;34m1\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ inputs[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.conv (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │        \u001b[38;5;34m216\u001b[0m │ reshape[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│                     │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.bn             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │         \u001b[38;5;34m96\u001b[0m │ stem.conv[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stem.act            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ stem.bn[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │        \u001b[38;5;34m216\u001b[0m │ stem.act[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mDepthwiseConv2D\u001b[0m)   │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp.… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │         \u001b[38;5;34m96\u001b[0m │ stage1.mbconv1.d… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.dp.… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m400\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ stage1.mbconv1.d… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ max_pooling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m200\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ stage1.mbconv1.d… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m24\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.se.… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m24\u001b[0m)  │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ stage1.mbconv1.se.… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m6\u001b[0m)   │        \u001b[38;5;34m150\u001b[0m │ stage1.mbconv1.s… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">32,192</span> (125.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,192\u001b[0m (125.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,912</span> (120.75 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,912\u001b[0m (120.75 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> (5.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,280\u001b[0m (5.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = nse.models.efficientnet.efficientnetv2_from_object(\n",
    "    x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n",
    "    params=architecture.params,\n",
    "    num_classes=len(class_names)\n",
    ")\n",
    "model.summary(layer_range=('inputs', model.layers[10].name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model\n",
    "\n",
    "Now let's train the model using the LSAD dataset. We will train the model for 100 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sorting lsad labels: 100%|██████████| 36120/36120 [00:07<00:00, 4746.99it/s]\n",
      "Sorting lsad labels: 100%|██████████| 36120/36120 [00:07<00:00, 4552.33it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Validation steps per epoch: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">78</span>                                                             <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">datasets.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py#105\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">105</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Validation steps per epoch: \u001b[1;36m78\u001b[0m                                                             \u001b]8;id=977579;file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py\u001b\\\u001b[2mdatasets.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=747916;file:///workspaces/heartkit/heartkit/tasks/rhythm/datasets.py#105\u001b\\\u001b[2m105\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Saving validation dataset to <span style=\"color: #800080; text-decoration-color: #800080\">/tmp/hk-4-stage-rhythm/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">val.tfds</span>                                   <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">train.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#57\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">57</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Saving validation dataset to \u001b[35m/tmp/hk-4-stage-rhythm/\u001b[0m\u001b[95mval.tfds\u001b[0m                                   \u001b]8;id=337186;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=380060;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#57\u001b\\\u001b[2m57\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   0%|           0/100 ETA: ?s,  ?epochs/sWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723841770.965635  789335 service.cc:146] XLA service 0x79d8f400b520 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723841770.965656  789335 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n",
      "I0000 00:00:1723841777.014004  789335 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "Training: 100%|██████████ 100/100 ETA: 00:00s,   1.94s/epochs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 806us/step\n",
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 915us/step - acc: 0.9442 - f1: 0.9444 - loss: 0.0903\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>VAL SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9444</span>, <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9445</span>, <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0920</span>                                                  <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">train.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#190\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">190</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9444\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9445\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0920\u001b[0m                                                  \u001b]8;id=498948;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py\u001b\\\u001b[2mtrain.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=160307;file:///workspaces/heartkit/heartkit/tasks/rhythm/train.py#190\u001b\\\u001b[2m190\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.train(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now that we have trained the model, we will evaluate the model on the test dataset. Similar to training, we will provide the same task parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Loading validation dataset from <span style=\"color: #800080; text-decoration-color: #800080\">/tmp/hk-4-stage-rhythm/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">val.tfds</span>                             <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">evaluate.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#33\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">33</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Loading validation dataset from \u001b[35m/tmp/hk-4-stage-rhythm/\u001b[0m\u001b[95mval.tfds\u001b[0m                             \u001b]8;id=234053;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=146316;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#33\u001b\\\u001b[2m33\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m78/78\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - acc: 0.9442 - f1: 0.9444 - loss: 0.0903\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TEST SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9444</span>, <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9445</span>, <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0920</span>                                               <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">evaluate.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#50\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">50</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9444\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9445\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0920\u001b[0m                                               \u001b]8;id=844962;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=167414;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#50\u001b\\\u001b[2m50\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m624/624\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n",
      "\u001b[1m613/613\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - acc: 0.9518 - f1: 0.9520 - loss: 0.0804\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TEST SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">THRESH</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50.00</span>%, <span style=\"color: #808000; text-decoration-color: #808000\">DROP</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.76</span>%                                                        <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">evaluate.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#62\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">62</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mTHRESH\u001b[0m=\u001b[1;36m50\u001b[0m\u001b[1;36m.00\u001b[0m%, \u001b[33mDROP\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.76\u001b[0m%                                                        \u001b]8;id=225772;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=800581;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#62\u001b\\\u001b[2m62\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TEST SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9520</span>, <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9521</span>, <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0822</span>                                               <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">evaluate.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#63\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">63</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9520\u001b[0m, \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9521\u001b[0m, \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0822\u001b[0m                                               \u001b]8;id=376417;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py\u001b\\\u001b[2mevaluate.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=888662;file:///workspaces/heartkit/heartkit/tasks/rhythm/evaluate.py#63\u001b\\\u001b[2m63\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.evaluate(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix\n",
    "\n",
    "Let's visualize the confusion matrix to understand the model's performance on each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.Image(filename=params.job_dir / \"confusion_matrix_test.png\", width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export model to TF Lite / TFLM\n",
    "\n",
    "Once we have trained and evaluated the model, we need to export the model into a format that can be used for inference on the edge. Currently, we export the model to TensorFlow Lite flatbuffer format. This will also generate a C header file that can be used with TensorFlow Lite for Microcontrollers (TFLM).\n",
    "\n",
    "\n",
    "### Apply post-training quantization (PTQ)\n",
    "\n",
    "For running on bare metal, we will perform post-training quantization to convert the model to an 8-bit integer model. The weights and activations will be quantized to 8-bits and biases will be quantized to 32-bits. This will reduce the model size and improve the inference speed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantization = hk.QuantizationParams(\n",
    "    enabled=True,\n",
    "    format=\"INT8\",\n",
    "    io_type=\"int8\",\n",
    "    conversion=\"KERAS\",\n",
    ")\n",
    "params.quantization = quantization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1723841958.715237  789182 tf_tfl_flatbuffer_helpers.cc:392] Ignored output_format.\n",
      "W0000 00:00:1723841958.715249  789182 tf_tfl_flatbuffer_helpers.cc:395] Ignored drop_control_dependency.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Validating model results                                                                      <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#94\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">94</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Validating model results                                                                      \u001b]8;id=236251;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=372244;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#94\u001b\\\u001b[2m94\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\n",
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TF METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2360</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9657</span> <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9761</span>                                                <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#101\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">101</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTF METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2360\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9657\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9761\u001b[0m                                                \u001b]8;id=166258;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=873474;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#101\u001b\\\u001b[2m101\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TFL METRICS<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2441</span> <span style=\"color: #808000; text-decoration-color: #808000\">ACC</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9639</span> <span style=\"color: #808000; text-decoration-color: #808000\">F1</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9752</span>                                               <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#102\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">102</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTFL METRICS\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.2441\u001b[0m \u001b[33mACC\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9639\u001b[0m \u001b[33mF1\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9752\u001b[0m                                               \u001b]8;id=458215;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=444742;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#102\u001b\\\u001b[2m102\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Validation passed <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0080</span><span style=\"font-weight: bold\">)</span>                                                                   <a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">export.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#110\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">110</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m Validation passed \u001b[1m(\u001b[0m\u001b[1;36m0.0080\u001b[0m\u001b[1m)\u001b[0m                                                                   \u001b]8;id=131787;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py\u001b\\\u001b[2mexport.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=175493;file:///workspaces/heartkit/heartkit/tasks/rhythm/export.py#110\u001b\\\u001b[2m110\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n",
    "with open(os.devnull, 'w') as devnull:\n",
    "    with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n",
    "        task.export(params=params)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run inference demo\n",
    "\n",
    "We will run a demo on the PC to verify that the model is working as expected. The demo will load the model and run inferences across a randomly selected ECG signal. The demo will also provide the model's prediction and the corresponding class name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Inference: 100%|██████████| 1/1 [00:00<00:00,  1.86it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.demo(params=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
